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Pigeon-inspired optimization and extreme learning machine via wavelet packet analysis for predicting bulk commodity futures prices

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Abstract

In this paper, a hybrid approach consisting of pigeon-inspired optimization (PIO) and extreme learning machine (ELM) based on wavelet packet analysis (WPA) is presented for predicting bulk commodity futures prices. Firstly, WPA is applied to decompose the original futures prices into a set of lower-frequency subseries. Secondly, the PIO algorithm is used to optimize the parameters of ELM and then the optimized ELM is utilized to forecast the subseries. Finally, we adopt the hybrid method to calculate the final forecasting outcomes of futures prices. In order to further test the predictive ability of the hybrid forecasting model on bulk commodity futures prices, we use the prices of West Texas Intermediate crude oil futures and Chicago Board of Trade soybean futures to make one-step, two-step and four-step ahead predictions. In comparison with complete ensemble empirical mode decomposition with adaptive noise, empirical mode decomposition and singular spectrum analysis, WPA is the most suitable for decomposing bulk commodity futures prices. The experimental outcomes show that the hybrid WPA-PIO-ELM model has better performance on horizontal precision, directional precision and robustness.

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References

  1. Chi G T, Li Z J. Forecast model of stock index futures prices based on small sample. ICIC Express Lett Part B Appl Int J Res Surv, 2014, 5: 657–662

    Google Scholar 

  2. Wang C Y. Forecast on price of agricultural futures in China based on ARIMA model. Asian Agr Res, 2016, 8: 9–12

    Google Scholar 

  3. Darekar A, Reddy A. Predicting market price of soybean in major india studies through ARIMA model. Soc Sci Electron Publishing, 2017, 30: 73–76

    Google Scholar 

  4. Xu W C, Zhou H B, Cheng N, et al. Internet of vehicles in big data era. IEEE/CAA J Autom Sin, 2018, 5: 19–35

    Article  Google Scholar 

  5. Mazouchi M, Naghibi-Sistani M B, Sani S K H. A novel distributed optimal adaptive control algorithm for nonlinear multi-agent differential graphical games. IEEE/CAA J Autom Sin, 2018, 5: 331–341

    Article  MathSciNet  Google Scholar 

  6. Zhang Y H, Shen X, Shen T L. A survey on online learning and optimization for spark advance control of SI engines. Sci China Inf Sci, 2018, 61: 070201

    Article  MathSciNet  Google Scholar 

  7. Li H T, Zhao G D, Meng M, et al. A survey on applications of semi-tensor product method in engineering. Sci China Inf Sci, 2018, 61: 010202

    Article  MathSciNet  Google Scholar 

  8. Baruník J, Malinská B. Forecasting the term structure of crude oil futures prices with neural networks. Appl Energy, 2016, 164: 366–379

    Article  Google Scholar 

  9. Hu J W S, Hu Y C, Lin R R W. Applying neural networks to prices prediction of crude oil futures. Math Probl Eng, 2012, 2012: 1–12

    Google Scholar 

  10. Zhang X M, Han Q L, Zeng Z. Hierarchical type stability criteria for delayed neural networks via canonical bessellegendre inequalities. IEEE Trans Cybern, 2018, 48: 1660–1671

    Article  Google Scholar 

  11. Zhang Y, He J, Yin T F. Research on petroleum price prediction based on SVM. Comput Simul, 2012, 29: 375–377

    Google Scholar 

  12. Zhang J L, Zhang Y J, Zhang L. A novel hybrid method for crude oil price forecasting. Energy Econ, 2015, 49: 649–659

    Article  Google Scholar 

  13. Das S P, Padhy S. A novel hybrid model using teaching-learning-based optimization and a support vector machine for commodity futures index forecasting. Int J Mach Learn Cyber, 2018, 9: 97–111

    Article  Google Scholar 

  14. Ela A A A E, Abido M A, Spea S R. Differential evolution algorithm for optimal reactive power dispatch. Electric Power Syst Res, 2011, 81: 458–464

    Article  Google Scholar 

  15. Zhang D F, Duan H B, Yang Y J. Active disturbance rejection control for small unmanned helicopters via Levy flight-based pigeon-inspired optimization. Aircraft Eng Aerospace Tech, 2017, 89: 946–952

    Article  Google Scholar 

  16. Chen H H, Chen M, Chiu C C. The integration of artificial neural networks and text mining to forecast gold futures prices. Commun Stat-Simul Comput, 2016, 45: 1213–1225

    Article  MathSciNet  MATH  Google Scholar 

  17. Dou R, Duan H B. Pigeon inspired optimization approach to model prediction control for unmanned air vehicles. Aircraft Eng Aerospace Tech, 2016, 88: 108–116

    Article  Google Scholar 

  18. Pang B, Liu M, Zhang X, et al. A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts. Sci China Inf Sci, 2018, 61: 022312

    Article  MathSciNet  Google Scholar 

  19. Sun S L, Wang S Y, Zhang G W, et al. A decomposition-clustering-ensemble learning approach for solar radiation forecasting. Sol Energy, 2018, 163: 189–199

    Article  Google Scholar 

  20. Liu H, Mi X W, Li Y F. Comparison of two new intelligent wind speed forecasting approaches based on wavelet packet decomposition, complete ensemble empirical mode decomposition with adaptive noise and artificial neural networks. Energy Convers Manag, 2018, 155: 188–200

    Article  Google Scholar 

  21. Jiang F, He J Q, Zeng Z G, et al. A decomposition-optimization-ensemble learning approach for electricity price forecasting (in Chinese). Sci Sin Inform, 2018, 48: 1300–1315

    Article  Google Scholar 

  22. Wang D Y, Yue C Q, Wei S, et al. Performance analysis of four decomposition-ensemble models for one-day-ahead agricultural commodity futures price forecasting. Algorithms, 2017, 10: 108

    Article  MathSciNet  MATH  Google Scholar 

  23. Wang Y, Qi C, Li M F. Prediction of commodity prices based on SSA-ELM. Syst Eng-Theory Pract, 2017, 37: 2004–2014

    Google Scholar 

  24. Lu H F. Price forecasting of stock index futures based on a new hybrid EMD-RBF neural network model. Agro Food Ind Hi Tech, 2017, 28: 1744–1747

    Google Scholar 

  25. Wang J, Li X. A combined neural network model for commodity price forecasting with SSA. Soft Comput, 2018, 22: 5323–5333

    Article  Google Scholar 

  26. Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70: 489–501

    Article  Google Scholar 

  27. Duan H B, Qiao P X. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int J Intel Comput Cyber, 2014, 7: 24–37

    Article  MathSciNet  Google Scholar 

  28. Diebold F X, Mariano R S. Comparing predictive accuracy. J Business Economic Stat, 2002, 20: 134–144

    Article  MathSciNet  Google Scholar 

  29. Pesaran M H, Timmermann A. A simple nonparametric test of predictive performance. J Business Economic Stat, 1992, 10: 461–465

    Google Scholar 

  30. Helske J, Luukko P. Ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN). Int J Public Health, 2016, 60: 1–9

    Google Scholar 

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Authors and Affiliations

  1. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China

    Feng Jiang & Jiaqi He

  2. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Zhigang Zeng

Authors
  1. Feng Jiang
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  2. Jiaqi He
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  3. Zhigang Zeng
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Correspondence to Feng Jiang or Zhigang Zeng.

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Jiang, F., He, J. & Zeng, Z. Pigeon-inspired optimization and extreme learning machine via wavelet packet analysis for predicting bulk commodity futures prices. Sci. China Inf. Sci. 62, 70204 (2019). https://doi.org/10.1007/s11432-018-9714-5

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